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The TBM wake-up call: Multicloud networking is now the business nervous system

By 2026, Technology Business Management (TBM) leaders will find that their biggest lever for cost, risk, and innovation is no longer just which clouds they choose, but how those clouds are wired together. Hybrid and multicloud are already the norm: IDC reports that by late 2024, nearly nine in ten enterprises were operating hybrid cloud environments, with close to 80 percent using more than one public cloud provider. IDC Blog Gartner expects that roughly 90 percent of organizations will adopt a hybrid cloud approach by 2027, making cross-cloud connectivity and data synchronization a frontline concern for infrastructure and operations leaders. Gartner

This shift has made multicloud networking the de facto control plane for TBM. It is the mesh through which AI workloads move, SaaS invoices accumulate, security risks propagate, and real-time telemetry for cost allocation is collected. At the same time, it is becoming harder to manage. Different cloud providers were never designed to interoperate elegantly, resulting in inconsistent constructs, fragmented observability and complex routing patterns that drive both operational and financial drag. Interconnections – The Equinix Blog+1

In this context, the six headline technologies for Technology Business Management in 2026 – agentic AI, AI-native development platforms, preemptive cybersecurity, quantum computing, connected ecosystems (IoT), and edge computing – will not live in isolation. Their real impact will be measured by how well they are embedded into a multicloud networking fabric, and how that fabric is, in turn, governed by TBM disciplines.

The story of multicloud networking over the next two years is therefore not just about faster links or clever routing. It is about building a programmable, policy-driven, AI-infused network-of-clouds that can be measured, optimized and secured in business terms, not just technical metrics.

Agentic AI: From scripts and playbooks to autonomous multicloud stewards

Agentic AI – AI systems that can pursue goals, plan multi-step actions and orchestrate tools with limited human supervision – is rushing from theory to production. Info-Tech Research Group describes agentic AI platforms as environments that enable autonomous digital agents to perceive their surroundings, make context-sensitive decisions and orchestrate complex workflows across multiple systems. Info-Tech Research Group Major cloud providers are taking notice: Amazon Web Services created a dedicated agentic AI group in 2025, positioning it as a potential multi-billion-dollar business that will automate many aspects of digital operations. Reuters

For multicloud networking, agentic AI changes the scale and pace of management. Instead of human engineers juggling thousands of route tables, security groups, and cost anomalies, TBM teams can increasingly rely on swarms of AI agents operating inside guardrails. These agents watch traffic flows, performance metric,s and cost trends across clouds, then propose or execute changes – from rebalancing workloads between regions to renegotiating interconnect commitments.

In a 2026 TBM scenario, a network-focused agent might continuously simulate egress charges, latency and resilience impacts of different connectivity topologies. If it detects that generative AI inference traffic is spiking in a region where provider A is suddenly more expensive than provider B, it could automatically recommend shifting to private interconnect or bringing in a neutral interconnection provider to optimize both performance and cost. Because agentic AI can integrate billing APIs, observability data and policy repositories, it can reason across layers that have historically been siloed.

However, as Info-Tech warns, winning with agentic AI requires careful calibration of human oversight and automation. Info-Tech Research Group TBM leaders will need to define which financial decisions AI agents can make independently – such as fine-tuning bandwidth reservations – and where sign-offs are mandatory, such as committing to multi-year connectivity contracts or moving sensitive data to new jurisdictions. This is where TBM frameworks and chargeback models become the “constitution” that agentic AI must follow inside the multicloud network fabric.

AI-native development platforms: Baking network and cost intelligence into code

The rise of AI-native development platforms – where AI copilots and agents assist across the software lifecycle – is reshaping how applications interact with networks. Beyond writing code, these platforms can now generate infrastructure-as-code templates, define service meshes and embed FinOps policies directly into deployment pipelines.

Gartner has highlighted multicloud and cross-cloud as a key trend shaping the future of cloud, warning that many organizations struggle to get expected results from their multicloud implementations because of connectivity and interoperability challenges. APMdigest AI-native platforms can help by generating consistent configurations for networking and security that apply across providers, shrinking the gap between design and reality.

For example, a development team building an AI-powered analytics service might simply declare its intent in a high-level specification: “This service must reside within jurisdictions X and Y, maintain latency under 50 milliseconds to edge gateways in these markets, and keep data egress below a specified monthly budget.” The AI-native platform could then generate not only the Kubernetes manifests and serverless functions, but also the multicloud networking policies – from virtual private cloud peering to gateway placements – that satisfy those constraints.

In a TBM-aware setup, these platforms also become cost-literate. They surface real-time feedback to developers about how architectural choices affect cross-cloud data transfer fees, inter-region replication costs and content delivery spending. Multicloud networking stops being invisible “plumbing” and becomes a first-class design dimension. Over time, this will reduce the number of expensive “re-architecture” projects triggered by unsustainable network bills discovered after go-live.

Preemptive cybersecurity: Securing AI-driven multicloud fabrics before attacks land

As multicloud networking grows more complex, the attack surface expands. Lateral movement between clouds, misconfigured APIs and shadow connectivity paths can all become vectors for attackers. Traditional detect-and-respond approaches struggle to keep up, especially when threats leverage their own agentic AI.

Gartner describes preemptive cybersecurity as an emerging strategy focused on preventing and deterring attacks before they can launch or succeed, using capabilities that deny, disrupt and deceive attackers rather than simply reacting after alerts fire. Gartner+1 Other vendors talk about “preemptive cyber defense” – systems that neutralize threats before execution instead of relying on endpoint or extended detection and response. Morphisec+1

Applied to multicloud networking, preemptive cybersecurity means embedding continuous attack surface mapping, path analysis and deception into the network itself. In 2026, TBM leaders should expect their multicloud networking platforms to:

First, maintain real-time inventories of all public and private endpoints, routes, and interconnects spanning providers, and automatically correlate those to business services and owners for accountability.

Second, use AI models to predict which exposed paths are most likely to be targeted, based on threat intelligence and behavioral baselines, and automatically implement controls ranging from micro-segmentation to just-in-time access revocation.

Third, deploy deception techniques – such as decoy services and fake data paths – at the network layer to mislead attackers and gain early warning.

Crucially, preemptive cybersecurity must be expressed in financial and risk terms that TBM can govern. A preemptive control that significantly raises cloud provider data processing charges or routing overhead may not be acceptable unless it demonstrably reduces the probability of high-impact incidents. This is where TBM dashboards need to fuse security posture scores with cost-per-control metrics, enabling informed trade-offs rather than blind overprotection or risky cost-cutting.

Quantum computing: Stress-testing multicloud decisions at planetary scale

Quantum computing is still emerging, but by 2026, more enterprises will start piloting quantum-inspired and early quantum-accelerated workloads for specific optimization and simulation problems. In the context of multicloud networking and TBM, its most interesting use may not be running production business logic, but acting as a powerful “what-if” engine.

Multicloud networking presents a huge optimization surface: choosing which provider, region, and path to use for specific workloads while balancing cost, carbon, latency, and compliance constraints across thousands of variables. Classical algorithms already struggle to find global optima in this space; quantum and quantum-inspired algorithms promise to explore far more combinations in a fraction of the time.

A TBM team could use quantum optimization to explore questions such as:

• What is the optimal mix of private interconnection, cloud-native connectivity and third-party network-as-a-service to minimize total cost of ownership for AI training, given rapidly changing spot prices and carbon intensity across regions?
• Which combination of data center regions and edge locations yields the best trade-off between customer experience and sovereign data requirements, under tightening regulations?
• While full-scale quantum advantage for these problems may still be years away, quantum-inspired solvers available via cloud APIs can already help stress-test strategies that TBM and network architects design today. The key is to integrate their outputs into the same multicloud dashboards and policy engines that guide human and agentic AI decisions, rather than treating quantum experiments as isolated science projects.

Connected ecosystems and edge: Multicloud networking moves to the factory floor and city street

The Internet of Things is evolving into dense, connected ecosystems spanning factories, cities, vehicles and supply chains. Edge computing has emerged as the preferred way to process the resulting data deluge by pushing compute closer to where data is generated. Research from StartUs Insights and others finds that edge computing is enabling low-latency applications in autonomous vehicles, smart cities and industrial automation, with large enterprises rapidly adopting it to reduce latency and increase efficiency. StartUs Insights

For TBM and multicloud networking, this means the “edges” of the network are no longer just distant outposts; they are revenue-critical domains in their own right. An industrial firm might run computer vision models on rugged edge servers in ports and factories, relay aggregated insights to regional clouds for coordination, and archive batches to a low-cost hyperscaler region for long-term analytics. Telcos and logistics companies are layering private 5G and time-sensitive networking onto this picture, multiplying the number of endpoints that TBM must track and cost-allocate.

Modern multicloud networking platforms are responding by extending their control planes to the edge, offering unified policies that span on-premises clusters, multiple clouds and thousands of edge locations. IDC expects demand for multicloud networking to accelerate as enterprises seek to connect and secure AI models and inferencing applications across these distributed estates. IDC Futuriom’s research similarly positions multicloud networking and network-as-a-service at the core of future IT infrastructure strategies. Itential

TBM leaders must therefore evolve their cost models to treat edge sites as first-class assets with their own bill-of-IT, not as “miscellaneous connectivity expenses.” That includes understanding how decisions like where to terminate edge traffic (locally, regionally, or centrally) and which cloud to use for backhaul materially affect margins, service-level agreements, and sustainability targets.

Re-imagining TBM: From cost policing to dynamic policy for AI-driven networks

As these technologies converge, TBM’s role in multicloud networking becomes more strategic and more continuous. Instead of annual budgeting cycles and quarterly cloud optimization sprints, TBM teams will be expected to define dynamic policies that AI agents, developers and network platforms can enforce in near real time.

Those policies will span several dimensions. They will define acceptable ranges for unit economics, such as the cost per gigabyte of egress across specific clouds or the cost per millisecond of latency improvement for customer-facing services. They will codify risk tolerances for preemptive security controls and data residency. They will establish thresholds that trigger automated actions, such as scaling down underutilized interconnects or renegotiating enterprise agreements when usage patterns shift.

To support this, TBM tooling will need deeper hooks into multicloud networking platforms. Rather than treating network charges as opaque lines on a bill, these tools should ingest detailed flow logs, service topology data and security posture metrics. Cloud-agnostic monitoring and multicloud connectivity vendors are increasingly investing in such capabilities, positioning themselves as the “glass panels” where technical and financial perspectives align. Netskope

The cultural shift may be even bigger than the technical one. TBM teams will need to collaborate more closely with SRE, NetOps, SecOps, and platform engineering groups, as well as with business product owners. Agentic AI will automate many of the rote analyses TBM teams perform today, but it will also force them to articulate their reasoning in machine-readable form. In effect, TBM moves from being a specialist practice to becoming the business grammar of the multicloud network.

Closing thoughts and looking forward

By 2026, multicloud networking will be the stage on which the six defining technologies of TBM – agentic AI, AI-native development platforms, preemptive cybersecurity, quantum computing, connected ecosystems, and edge computing – all play out. The organizations that thrive will be those that recognize multicloud networking not as background plumbing, but as a strategic, programmabl,e and measurable asset that can be shaped in real time to support business goals.

Agentic AI will help manage the growing complexity, taking on operational workloads that once overwhelmed human teams. AI-native platforms will ensure that every new application is born with an awareness of network and cost realities. Preemptive cybersecurity will make the multicloud fabric safer by design, not just better monitored. Quantum computing will offer new ways to explore the vast optimization space of cross-cloud decisions. Connected IoT ecosystems and edge sites will expand TBM’s remit beyond cloud invoices into the physical world.

The common thread is governance. Without clear TBM policies, transparent telemetry and shared accountability, these advances risk adding yet more complexity and cost. With them, multicloud networking becomes the nervous system of a more autonomous, resilient, and efficient digital enterprise.

Over the next 18 to 24 months, TBM leaders should prioritize three actions: establishing unified visibility into multicloud networking spend and performance; piloting agentic AI use cases with strong guardrails; and partnering with security and platform teams to embed preemptive, policy-driven controls into the network itself. Do that, and multicloud networking in 2026 will not just be a cost center to tame, but the foundation for bolder, more profitable digital bets.

Reference sites

“Multicloud Connectivity: A Complete Guide” – Megaport Blog – https://www.megaport.com/blog/multicloud-connectivity-complete-guide/

“Multicloud Networking: Definitions, Benefits, and Challenges” – Kentik – https://www.kentik.com/kentipedia/multicloud-networking/

“Ten Trends That Shaped the Cloud Market in 2024” – IDC Blog – https://blogs.idc.com/2025/02/05/ten-trends-that-shaped-the-cloud-market-in-2024/

“Don’t Delay in Building Preemptive Cybersecurity Solutions” – Gartner – https://www.gartner.com/en/articles/preemptive-cybersecurity-solutions

“Edge Computing Report 2025” – StartUs Insights – https://www.startus-insights.com/innovators-guide/edge-computing-report/

Benoit Tremblay, Author, IT Security & Business Management, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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